FEATURE
Time, technology, talent: The three- pronged promise of cloud ML Cloud ML appears to be the future of AI, and the future seems bright
Sudi Bhattacharya, Ashwin Patil, Jonathan Holdowsky, and Diana Kearns-Manolatos
THE DELOITTE CENTER FOR INTEGRATED RESEARCH Time, technology, talent: The three-pronged promise of cloud ML
Start with the business case, assess your data and model requirements, invest in the right talent, and be willing to fail fast to reap the maximum benefits from cloud machine learning.
S ORGANIZATIONS LOOK to use machine services, and platforms—including pretrained learning (ML) to enhance their business models and accelerators—that provide more Astrategies and operations, the retailer options for how data science and engineering Wayfair looked to the cloud. Wayfair believes what teams can collaborate to bring models from the lab so many other organizations seem to—that the to the enterprise. Indeed, because of all these cloud and the fully integrated suite of services it advantages, time, technology, and talent—which offers may give organizations the best approach to might otherwise serve as challenges to scaling create ML solutions with time to market, existing enterprise technology—comprise the three- resources, and available technologies. For its part, pronged promise of cloud ML (see sidebar, “The Wayfair pursued the cloud for its scalability—key measurable value of cloud ML”). for retailers with varying demand levels—as well as a full suite of integrated analytics and productivity The recognition of this potential has fueled rapid tools service offerings to drive actionable insights. growth: The current cloud ML market is estimated The results? Faster insights into changing market to be worth between US$2 billion and US$5 billion, conditions to save time. Immediate access to the with the potential to reach US$13 billion by 2025. most current ML technologies. And tools of What such growth says is that adopters are productivity that can drive efficient talent.1 increasingly realizing the benefits of cloud ML and that it is the future of artificial intelligence (AI). Indeed, ML is transforming nearly every industry, And with just 5% of the potential cloud ML market helping companies drive efficiencies, enable rapid being penetrated according to one estimate, the innovation, and meet customer needs at an community of adopters should only deepen across unprecedented pace and scale. As companies sectors and applications.2 increasingly mature in their use of ML, the heavy reliance on large data sets and the need for fast, To explore the potential cloud ML represents for reliable processing power are expected to drive the organizations looking to innovate, we conducted future of ML into the cloud. interviews with nine leaders in cloud and AI whose perspectives largely inform the content that follows. The well-known benefits of the cloud—including In this paper, we provide an overview of three modular, elastic, rapidly deployable, and scalable distinct operating models for cloud ML and offer infrastructure without heavy upfront investment— insights for executives as they seek to unlock the apply when bringing ML into the cloud. Cloud ML full potential of the technology. additionally introduces cutting-edge technologies,
2 Cloud ML appears to be the future of AI, and the future seems bright
THE MEASURABLE VALUE OF CLOUD ML In the 2020 Deloitte State of AI in the Enterprise survey, 83% of respondents said that AI will be critically or very important to their organizations’ success within the next two years. Around two- thirds of respondents said they currently use ML as part of their AI initiatives. And our survey suggests that a majority of these AI/ML programs currently use or plan to use cloud infrastructure in some form. Our additional analysis of the survey data for this research showed that cloud ML, specifically, drives measurable benefits for the AI program (figure 1) and generally improves outcomes as compared to nonspecific AI deployments:
• Forty-nine percent cloud ML users said they saw “highly” improved process efficiencies (vs. 42% overall survey results)
• Forty-five percent saw “highly” improved decision-making (vs. 39% overall)
• Thirty-nine percent experienced “significant” competitive advantage (vs. 26% overall)
Importantly, these data points showed that cloud ML tools appear to make a notable impact on longer-term, strategic areas, such as competitive advantage and improved decision-making, when compared to general AI adoption. This is an important distinction and shift in the approach organizations are taking to run their AI programs (away from on-premise AI platforms), and a trend that is expected to continue as cloud ML takes greater focus for global organizations.
The addition of low-code/no-code AI tools, which are part of some cloud ML service offerings, can extend these advantages with responses showing additional gains in decision-making, process efficiencies, and competitive advantage and other areas. For instance:
• Fifty-six percent of low-code/no-code cloud ML users said they saw “highly” improved process efficiencies (vs. 49% in cloud ML users vs. 42% overall)
• Fifty-four percent saw “highly” improved decision-making (vs. 45% in cloud ML users vs. 39% overall)
• Forty-nine percent experienced “significant” competitive advantage (vs. 39% in cloud ML users vs. 26% overall)
• Low-code/no-code cloud ML users saw additional gains in areas that general cloud ML users did not see with outcomes such as new insights and improved employee productivity, among others.
The emergence of low-code/no-code tools is part of a broader trend toward greater accessibility to cloud AI/ML solutions and a more diverse user community. These positive survey results suggest that this trend may only strengthen in the near to medium term.
3 Time, technology, talent: The three-pronged promise of cloud ML
Cloud ML can drive improved outcomes in key areas of decision-making, process efficiencies, and competitive advantage
Total sur ey respondents loud- ased as a ser ice loud- ased ML loud- ased ML ith lo -code no-code
Percentage who achieved outcome to a “high” degree
Lowering cost 33% 32% 30% 29% Reducing headcount 31% 31% 30% 26% Improving decision-making 39% 42% 45% 54% Making processes more efficient 42% 46% 49% 56% Enhancing relationships with clients/ customers 36% 37% 38% 41% Making employees more productive 36% 38% 40% 49% Discovering new insights 36% 38% 40% 54% Enhancing existing products and services 37% 37% 37% 38% Creating new products and services 35% 36% 38% 40% Enabling new business models 34% 33% 32% 40% Enabling a “significant” lead over competitors 26% 34% 39% 49%
ote: s a prere uisite to participate, all , respondents to the sur ey must ha e adopted technologies in at least some fashion, ased on any of se eral criteria ee sur ey methodology for more detailed description of criteria ources: eena mmanath, a id ar is, and usanne upfer, Thri ing in the era of per asi e : eloitte s tate of in the nterprise, rd dition, eloitte nsights, uly , eloitte analysis
Deloitte Insights | deloitte.com/insights 4 Cloud ML appears to be the future of AI, and the future seems bright
Cloud ML: Adopting the right the organization is trying to solve, the technology approach context (to what degree the organization has data and models on-premises or in the cloud already), “AI is a tool to help solve a problem,” says Rajen and the talent context (to what extent the Sheth, vice president, AI, Google Cloud. “[In the organization has the right people resources in place past,] people were doing a lot of cool things with AI to build and train models). that didn’t solve an urgent problem. As a result, their projects stopped at the concept stage. We now There is no one-size-fits-all approach. Depending start with a business problem and then figure out on the business problem, multiple approaches may how AI solves it to create real value.” be employed at the same company. As Gordon Heinrich, senior solutions architect at Amazon Web As organizations look to advance their ML Services, says, “At the end of the day, it comes down programs supported by the cloud, there are three to where you can put ML and AI into a business basic approaches to cloud ML (figure 2) they can process that yields a better result than you are take—cloud AI platforms (model management in currently doing.” Building a model in-house the cloud); cloud ML services (including pretrained requires significant investment of time in data models); and AutoML (off-the-shelf models trained collection, feature engineering, and model with proprietary data). development. Depending on the specific use case and its strategic importance, companies can employ Selecting the right approach starts with cloud ML services, including pretrained models, to understanding the business context—what problem help speed up time to innovation and deliver results.
FIGURE 2 Cloud ML: Three core approaches and their benefits
Cloud AI platforms: Model management Cloud ML services: Pretrained AutoML: Customized in the cloud models and more pretrained models
What it is Brings new and Allows organizations to access Allows for “customizing” off-the- existing ML models pretrained models, frameworks, shelf versions of pretrained API into the cloud and general-purpose algorithms models with proprietary data including: Allows developers • Vision, speech, language, and Allows for ongoing model and data scientists video APIs refinement, which, in some cases, to use cloud-enabled • Virtual assistant and bot may even include a low-code or no- tools for training, frameworks code environment deployment, and • General-purpose algorithms model management for fraud detection, inventory management, call centers/ conversational AI, etc.
Benefits Helps manage cost Accelerates development of new Serves as a way to drive efficiency in of spot training, gain business applications without the life cycle of model deployment greater elasticity to the need for a large data science for organizations with talent scale infrastructure team to build the underlying constraints needs, and improve model and speeds time to 3 orchestration innovation Provides access to ML for across the model developers to get started quickly management life cycle and offers flexibility to shift to other operating models as needs change
Source: Deloitte analysis
5 Time, technology, talent: The three-pronged promise of cloud ML
“[In the past,] people were pretrained models instead of building their own doing a lot of cool things conversational AI models. with AI that didn’t solve Broadly speaking, there is no single approach to a cloud ML initiative. But any approach should an urgent problem. As contemplate a complex array of potentially deep a result, their projects and vast issues that are strategic, technical, and cultural in character and range the full arc of the stopped at the concept project lifespan. stage. We now start with a For general cloud ML use cases, companies should business problem and then think through comparable aspects of business, technology, and talent to determine the right figure out how AI solves it approach, while keeping in mind the following to create real value,” says high-level recommendations:
Rajen Sheth, vice president, • Business context: Define the artificial intelligence, problem statement.
Google Cloud. – The most impactful applications for cloud Conversational AI: This is one use case that ML typically start with identifying the came up repeatedly during our research where broader business problem AI is suited to cloud ML and pretrained models seem particularly help solve—whether it be scaling the digital well-positioned to help solve business, technology, business, enhancing customer experience, and talent challenges. Conversational AI is or addressing risk management challenges. frequently applied to help improve call center Executives should then examine the operations and enhance customer service. In fact, business problem from both an one estimate suggests that by 2025, the speech-to- industrywide and text technology will account for 40% of all inbound company-specific perspective. voice communications to call centers.4 • Technology context: Understand which Organizations can use conversational AI to support data/models you have and/or need and where customer service improvements in many ways— they reside. from helping answer customer questions directly via chatbots to supporting contact center staff in – Consider how long it will take to get access the background with next-best answer technology. to the data and to develop and test the These capabilities bring great value to customer models as it relates to time, resources, risks, service today. However, it takes a tremendous and deployment needs. Pretrained models amount of work, time, and resources to or AutoML solutions may offer time and continuously enhance conversational AI models, resource savings for initiatives that are less and it is not differentiating enough for most strategically differentiating for companies to build these models in house. So, most your business. companies are employing large cloud vendors’
6 Cloud ML appears to be the future of AI, and the future seems bright
– Consider program goals and outcomes. Use cases: Cloud ML adoption Starting small allows teams adequate time across industries to test and refine the model and also to operationalize and scale it over time. Think The broad applicability of cloud ML business about easy-to-achieve targets, mid- to archetypes has driven rapid adoption across longer-term business outcomes, and value. industries. Analysts, however, have indicated a standout opportunity for cloud ML to solve • Talent context: Assess data, ML, cloud, and business challenges at scale for industries with data science expertise across your team. significant call center footprints as well as supply chain challenges.5 – Review what talent you have to advance your ML initiatives and what talent you As Eduardo Kassner, chief technology and need to achieve your stated goals. innovation officer, One Commercial Partner Group, Microsoft, says, “Certainly, there is a big evolution – Assess areas where cloud ML services could in cloud ML services and APIs across a number of save training time, optimize the potential of industries. We are seeing increased activity in your existing talent, or fill in manufacturing where you bring together IoT, big resourcing gaps. data, and advanced analytics.”
Although not comprehensive by any means, In figure 3, we have compiled examples from six thoughtful evaluation of these three representative different industries where cloud ML has helped areas should point teams in the right direction as solve a range of tangible and definable business they assess which applications are most challenges. These companies have drawn on appropriate for cloud ML. It can also help ensure distinct cloud ML technology services such as valuable talent hours are utilized strategically speech and vision APIs and pretrained models for toward the company’s most differentiating features recommendation, fraud, and inventory and initiatives. management to advance their AI programs and achieve tangible business outcomes.
7 Time, technology, talent: The three-pronged promise of cloud ML
FIGURE 3 How different industries are reportedly benefiting from cloud ML adoption
Industry Organization Business use case Category
Financial services UBSa UBS created a digital app to allow financial advisors to have Recommendations personalized touchpoints with clients. It used an AI-driven personalization engine to customize the client experience around personal financial goals, milestones, and achievements. They believe this resulted in a significant uptick in high net worth and ultra-high net worth clients.
Financial services Capital Oneb Capital One analyzed large volumes of customer data to detect Fraud detection anomalous activity that could be indicative of fraud; alert customers when suspicious activity occurs; manage fraud reporting; and expedite new card deployment. They reported enhanced customer experience and minimized risks.
Life sciences and Takedac Takeda used clinical evidence to predict, diagnose, and treat Cloud-enabled AI/ health care nonalcoholic steatohepatitis (NASH) and treatment resistant ML toolkit depression (TRD). It used cloud ML to quickly and efficiently analyze a large data set with greater accuracy and rapidly tested predictive models to achieve nearly a 40% increase in accuracy, improving predictive accuracy from 53.4% to 92%.
Consumer Wayfaird Wayfair embraced cloud infrastructure to scale its retail operations, Customer (automotive, retail, enhance customer experience, and better manage a large network experience; transportation) of employees and suppliers. The shift began with a hybrid cloud inventory solution to handle burst capacity needs for an annual sale across management thousands of customers and millions of transactions. Wayfair then used additional data, AI, and ML services to support better and faster insights into consumer trends, patterns, and preferences to to drive real-time decisions related to merchandise forecasting, personalized customer experiences and recommendations, and targeted marketing promotions across 19 million active customers, 16,000 employees, and 11,000 suppliers.
Technology, Hulue Hulu reimagined its customer service experience across 25 million Speech API, media, and active subscribers. The cloud ML contact center and virtual agent virtual agent/ telecommunications helped engage users, resolve issues, escalate issues to a human bot framework, agent, and recommend the next-best action for cross-sell/up-sell conversational AI opportunities. The company reported a 50-second improvement on average handle time and improved customer service experience.
Government and US Navyf The US Navy modernized its vessel and facility repairs function by Vision API, public services using vision APIs to analyze drone-captured images of vessels and AutoML facilities. Using AutoML, it built a model that detected corrosion with a high degree of accuracy.
Energy, resources, Convoyg Convoy used cloud ML to analyze millions of shipping jobs versus Recommendations, and industrials/ trucker availability to recommend matches via a digital marketplace inventory consumer for carriers and drivers. This helped the company manage work management (automotive, retail, better and create greater process efficiency across the entire freight transportation) network. The company reported improved demand forecasting and reduced carbon emissions.
Sources: a. Tamara Cibenko, Amelia Dunlop, and Nelson Kunkel. Human experience platforms: Affective computing changes the rules of engagement, Deloitte Insights, January 15, 2020. b. AWS Machine Learning, “At Capital One, enhancing fraud protection with machine learning,” accessed November 26, 2020. c. Deloitte, “Cloud-based machine learning in health care creates patient-centered treatments,” accessed November 26, 2020. d. PRNewswire, “Wayfair chooses Google cloud to help scale its growing business, while creating engaging customer (and seller) experiences,” MARTECHSERIES, January 13, 2020. e. YouTube, “How AI is transforming the enterprise (Cloud Next ’19),” April 10, 2019. f. Stephanie Condon, “Federal contractor taps Google Cloud AI and ML to inspect US Navy vessels,” ZDNet, August 27, 2020. g. AWS Machine Learning, “Convoy is revolutionizing trucking through machine learning.”
8 Cloud ML appears to be the future of AI, and the future seems bright
Talent approach: Cloud ML is a PhD-level data science talent and thereby help team sport “democratize” ML. However, we recommend viewing these capabilities as a way to extend the reach of “When talking about talent in the AI/ML space, think deep data science expertise across a broader team of a staircase with four steps,” suggests Kassner. made up of new types of specialists. With cloud ML, “The first step is the developers who leverage pretrained models and AutoML tools have “hidden” cognitive services APIs in their applications. The the complexity and blurred the lines between next step is developers who know how to call APIs previously distinct roles. This often empowers ML for process automation, customer support, object engineers to fill the data science PhD talent gap and detection, among other scenarios of cognitive affords adopters greater flexibility in how they staff services. The third level in the talent staircase is teams. And, as these tools get more nuanced and developers who understand big data, data cataloging, powerful, such flexibility should only grow. data warehousing, data wrangling, and data lakes. Ultimately, however, as an organization’s modeling The fourth step is the one where specific algorithms needs evolve and become more complex, a PhD- and customer models are to be created with ML and trained data scientist may become indispensable. AI Ops, which may require data scientists or data analysts.” MLOps and governance: To achieve the business objectives of any cloud ML Maintaining rigorous quality initiative, data science professionals typically work and efficiency with a well-balanced talent team—cloud engineers, data engineers, and ML engineers. But PhD-level The term MLOps, or “machine learning operations,” data science talent is scarce.6 As a result—and as our refers to the steps that an organization takes as it interviewees repeatedly confirmed—many develops, tests, deploys, and monitors cloud ML companies are training a growing number of models over their life cycle. In effect, these steps developers and engineers to contribute alongside come to represent a set of governance practices them as non-PhD data scientists. that ensure integrity throughout the life cycle. As development operations (DevOps) become Some speculate that pretrained model and AutoML increasingly automated due to intensifying capabilities could reduce the need for scarce pressure for frequent model releases, these steps take on greater importance and urgency— especially as companies become more “When talking about talent cloud-focused in their infrastructure. in the AI/ML space, think of Commenting on the importance of this trend, a staircase with four steps,” Heinrich notes, “[MLOps] is about explainability says Eduardo Kassner, chief and getting a notification when a model is starting to drift. It is about making the data pipeline easier technology and innovation to use with data tagging and humans in the loop to achieve high-quality results. It really helps officer, One Commercial businesses feel more confident about putting Partner Group, Microsoft algorithms into production.”
9 Time, technology, talent: The three-pronged promise of cloud ML
While there is no single accepted global standard for MLOps process and best practices, a “[MLOps] is about comprehensive end-to-end MLOps program should explainability and getting a typically include four basic elements: versioning the model, autoscaling, continuous model notification when a model monitoring and training, and retraining and is starting to drift. It is redeployment (figure 4). about making the data Model bias: A related concept within a broader pipeline easier to use with ML governance program is model bias. In the past few years, we have seen instances of bias creeping data tagging and humans into AI/ML models. In fact, a recent experiment revealed that an AI vision model associated various in the loop to achieve high- pejorative stereotypes with gender, race, and other quality results. It really characteristics.7 ImageNet is one of a number of projects underway to identify and mitigate the role helps businesses feel more that bias plays in AI.8 confident about putting
According to Sheth, as cloud ML moves toward algorithms into production,” providing decision support or recommendations, says Gordon Heinrich, the issue of bias becomes even more acute because multiple human judgment factors are senior solutions architect at manufactured into the model. “There are tools that Amazon Web Services.
Four basic MLOps elements
CONTINUOUS VERSIONING RETRAINING AND 1 2 AUTOSCALING 3 MODEL MONITORING 4 THE MODEL REDEPLOYMENT AND TRAINING
e eloping a cloud ML nce deployed, a t is important to de elop Timely detection of drift model re uires model—as well as the effective ways to measure triggers the need for experimenting with supporting cloud model performance retraining and different data sets and infrastructure should e continuously to ensure it redeployment of the model algorithms to sol e the a le to scale up or scale produces uality results s model drift sets in, it same usiness pro lem down quickly as capacity This re uires consistent ecomes necessary to These data sets are demand fluctuates. e aluation of the model retrain the model on new ingested through a cloud utoscaling is a feature that output and monitoring of data and then to redeploy it pipeline n this sense, allows for an immediate drift and degradation o er reproduci ility is ey response to changing time This is ecause ersioning each data set, demand conditions, which e ternal factors e g , algorithm, and pipeline is may ultimately sa e macroeconomic conditions critical to ensure that adopters money to the eep changing, rendering reproduci ility e tent that they only pay for the data that formed the the capacity that they use initial training process o solete
ource: eloitte analysis Deloitte nsights deloitte.com/insights
10 Cloud ML appears to be the future of AI, and the future seems bright
help explain why the model gave a particular highest potential to benefit from AI outcome and where there are biases and where augmentation. The wrong process may lead to there aren’t and what impact such biases will have frustration and wasted resources. Gaining both on people in general. Organizations will have to dig awareness and enthusiastic buy-in from the deep to understand what is happening and highest level of leadership into these initiatives supplement the data accordingly. It’s a painstaking can help identify meaningful problems to solve process,” he says. and maximize success.
One cloud ML area where bias manifests frequently • Orchestrated talent. Remember that cloud is pretrained models. When developing and ML is a team sport. It is not just about training models in-house, the development team modeling. Investing in the right balance of data can implement safeguards and testing protocols to science and ML, cloud, and data engineering lessen the effect of bias since control of the process talent brings cloud ML to life. and training data rest with them. In contrast, they are less likely to be aware of bias when using • Cost savings and elasticity of cloud. Take pretrained models, as they have typically limited advantage of the opportunity cloud provides to visibility into how a model was trained and thus bring infrastructure cost from capex to opex. how much bias, if any, was present prior to model Given the extraordinary stress on infrastructure, deployment. Under such circumstances, testing for cloud provides the elasticity to use bias after model deployment requires heightened infrastructure on demand. scrutiny using a variety of known data sets so that the results can be detected based on their output. • Data requirements. Know the quality and (For more information on AI ethics and avoiding quantity of the training data you have and also bias, see Deloitte’s Trustworthy AITM framework.) what data you need and how you are going to get it.
Five key considerations to set • Importance of trial and error. Embrace up cloud ML initiatives the notion of “failing fast.” AI needs experimentation and innovation. It is not a Cloud ML can be transformational, and there is tried-and-tested recipe. strong evidence that organizations are already reaping its benefits in wide-ranging initiatives By putting some forethought into these key across industries, use cases, and archetypes. Based recommendations and gaining a thorough on our research and interviews with industry understanding of what they want to achieve, leaders, here are some important points for organizations can optimally employ cloud ML. organizations to keep in mind while starting out on They can thus reap the three-pronged promise of their cloud ML journey: cloud ML—extending the reach of critical ML talent and technology investments and cutting • Strategic alignment. Identify a business time to innovation. problem ripe for transformation that has the
11 Time, technology, talent: The three-pronged promise of cloud ML
Endnotes
1. Aithority, “Wayfair chooses Google Cloud to help scale its growing business, while creating engaging consumer experiences,” January 13, 2020; Angus Loten, “Pandemic has online sellers leaning on cloud,” Wall Street Journal, August 24, 2020.
2. Ed Anderson and David Smith, Hype cycle for cloud computing, 2020, Gartner, August 1, 2020.
3. MIT Sloan Management Review, “How AI changes the rules: New imperatives for the intelligent organization,” February 10, 2020.
4. Anthony Mullen, Bern Elliot, and Adrian Lee, Market guide for speech to text solutions, Gartner, April 22, 2020.
5. ABI Research, Cloud-based AI in a post-COVID-19 world, 2020.
6. Emilia Marius, “How to solve the talent shortage in data science,” InformationWeek, April 17, 2019.
7. Will Knight, “AI is biased. Here’s how scientists are trying to fix it,” Wired, December 19, 2019.
8. ImageNet website.
Acknowledgments
The authors wish to thank Rajen Sheth (Google Cloud), Eduardo Kassner (Microsoft, One Commercial Partner Group) and Gordon Heinrich (AWS), as well as Deloitte professionals David Kuder, James Ray, Jason Chiu, David Linthicum, and Scott Rodgers, for the time they spent in sharing their invaluable insights. The authors would also like to thank Lisa Beauchamp, Amy Gonzalez, Claire Melvin, and Saurabh Rijhwani for their marketing support. Negina Rood provided vital research support and Jay Parekh, critical analytical insights. Finally, this paper would not have been possible without the overarching and tireless support of Siri Anderson.
12 Cloud ML appears to be the future of AI, and the future seems bright
About the authors
Sudi Bhattacharya | [email protected]
Sudi Bhattacharya is a managing director with Deloitte Consulting LLP’s Cloud Engineering practice. He leads big data– and AI/ML-driven business process transformation projects and high-performance teams in public cloud environment for Fortune 500 businesses in finance, retail, CPG, and foodservice. A seasoned pragmatic thinker, Bhattacharya is adept at striking the right balance between developing a strategic vision and agile execution of that strategy in complex organizations with multiple competing priorities. He builds and leads high-performance teams that execute AI/ML augmented cloud-native application development projects on time and on budget.
Ashwin Patil | [email protected]
Ashwin Patil leads the Analytics and Information Management practice for the manufacturing sector at Deloitte Consulting LLP. He has more than 18 years of experience helping clients drive economic value in different functions by leveraging analytics and data. He has led large-scale, global implementations of warranty/quality solutions, supply chain risk and optimization solutions, customer segmentation and analytics solutions, sales and operations planning solutions, etc. He drives eminence in capability areas such as advanced analytics, big data, business intelligence, data warehousing, performance management, and enterprise information management.
Jonathan Holdowsky | [email protected]
Jonathan Holdowsky is a senior manager with Deloitte Services LP and part of Deloitte’s Center for Integrated Research, managing a wide array of thought leadership initiatives on issues of strategic importance to clients within the consumer and manufacturing sectors. His current research explores the promise of emerging technologies such as additive and advanced manufacturing, Internet of Things, Industry 4.0, cloud, and blockchain. Holdowsky recently served as a coauthor of Deloitte’s 2020 Global Blockchain Survey, among many other publications.
Diana Kearns-Manolatos | [email protected]
Diana Kearns-Manolatos is a senior manager in the Deloitte Center for Integrated Research where she analyzes market shifts and emerging trends across industries. Her research focuses on cloud and the future of workforce. Additionally, Kearns-Manolatos draws on almost 15 years of award-winning marketing communications expertise to align insights with business strategy. She speaks on technology and women in leadership and holds a bachelor’s and master’s degrees from Fordham University.
13 Time, technology, talent: The three-pronged promise of cloud ML
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Sudi Bhattacharya Managing director | Cloud Engineering | Deloitte Consulting LLP + 1 469 417 3240 | [email protected]
Sudi Bhattacharya is a managing director with Deloitte Consulting LLP’s Cloud Engineering practice.
Beena Ammanath Managing director | Deloitte Consulting LLP Executive director of the Deloitte AI Institute + 1 415 783 4562 | [email protected]
Beena Ammanath is a managing director with Deloitte Consulting LLP and serves as executive director of the Deloitte AI institute.
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Jonathan Holdowsky Senior manager | The Deloitte Center for Integrated Research | Deloitte Services LP + 1 617 437 3198 | [email protected]
Jonathan Holdowsky is a senior manager with Deloitte Services LP and part of Deloitte’s Center for Integrated Research, leading thought leadership initiatives that explore the promise of emerging and disruptive technologies.
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